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Geometric computer vision: omnidirectional visual and remotely sensed data analysis / Pouria Babahajiani (2021)
Titre : Geometric computer vision: omnidirectional visual and remotely sensed data analysis Type de document : Thèse/HDR Auteurs : Pouria Babahajiani, Auteur ; Moncef Gabbouj, Directeur de thèse Editeur : Tampere [Finlande] : Tampere University Année de publication : 2021 Importance : 147 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-952-03-1979-3 Note générale : bibliographie
Accademic Dissertation, Tampere University, Faculty of Information Technology and Communication Sciences FinlandLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effet de profondeur cinétique
[Termes IGN] espace public
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image panoramique
[Termes IGN] image Streetview
[Termes IGN] image terrestre
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle sémantique de données
[Termes IGN] réalité virtuelle
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] vision par ordinateur
[Termes IGN] zone urbaineIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Information about the surrounding environment perceived by the human eye is one of the most important cues enabled by sight. The scientific community has put a great effort throughout time to develop methods for scene acquisition and scene understanding using computer vision techniques. The goal of this thesis is to study geometry in computer vision and its applications. In computer vision, geometry describes the topological structure of the environment. Specifically, it concerns measures such as shape, volume, depth, pose, disparity, motion, and optical flow, all of which are essential cues in scene acquisition and understanding.
This thesis focuses on two primary objectives. The first is to assess the feasibility of creating semantic models of urban areas and public spaces using geometrical features coming from LiDAR sensors. The second objective is to develop a practical Virtual Reality (VR) video representation that supports 6-Degrees-of-Freedom (DoF) head motion parallax using geometric computer vision and machine learning. The thesis’s first contribution is the proposal of semantic segmentation of the 3D LiDAR point cloud and its applications. The ever-growing demand for reliable mapping data, especially in urban environments, has motivated mobile mapping systems’ development. These systems acquire high precision data and, in particular 3D LiDAR point clouds and optical images. A large amount of data and their diversity make data processing a complex task. A complete urban map data processing pipeline has been developed, which annotates 3D LiDAR points with semantic labels. The proposed method is made efficient by combining fast rule-based processing for building and street surface segmentation and super-voxel-based feature extraction and classification for the remaining map elements (cars, pedestrians, trees, and traffic signs). Based on the experiments, the rule-based processing stage provides substantial improvement not only in computational time but also in classification accuracy. Furthermore, two back ends are developed for semantically labeled data that exemplify two important applications: (1) 3D high definition urban map that reconstructs a realistic 3D model using input labeled point cloud, and (2) semantic segmentation of 2D street view images. The second contribution of the thesis is the development of a practical, fast, and robust method to create high-resolution Depth-Augmented Stereo Panoramas (DASP) from a 360-degree VR camera. A novel and complete optical flow-based pipeline is developed, which provides stereo 360-views of a real-world scene with DASP. The system consists of a texture and depth panorama for each eye. A bi-directional flow estimation network is explicitly designed for stitching and stereo depth estimation, which yields state-of-the-art results with a limited run-time budget. The proposed architecture explicitly leverages geometry by getting both optical flow ground-truths. Building architectures that use this knowledge simplifies the learning problem. Moreover, a 6-DoF testbed for immersive content quality assessment is proposed. Modern machine learning techniques have been used to design the proposed architectures addressing many core computer vision problems by exploiting the enriched information coming from 3D scene structures. The architectures proposed in this thesis are practical systems that impact today’s technologies, including autonomous vehicles, virtual reality, augmented reality, robots, and smart-city infrastructures.Note de contenu : 1- Introduction
2- Geometry in Computer Vision
3- Contributions
4- ConclusionNuméro de notice : 28323 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Computing and Electrical Engineering : Tempere, Finland : 2021 DOI : sans En ligne : https://trepo.tuni.fi/handle/10024/131379 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98342 Georeferencing with self-calibration for airborne full-waveform Lidar data using digital elevation model / Qinghua Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)
[article]
Titre : Georeferencing with self-calibration for airborne full-waveform Lidar data using digital elevation model Type de document : Article/Communication Auteurs : Qinghua Li, Auteur ; Jie Shan, Auteur Année de publication : 2021 Article en page(s) : pp 43 - 52 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] auto-étalonnage
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] forme d'onde pleine
[Termes IGN] géoréférencement
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] modèle numérique de surface
[Termes IGN] optimisation (mathématiques)
[Termes IGN] point d'appui
[Termes IGN] synchronisationRésumé : (Auteur) Precise georeferencing of airborne full-waveform lidar is a complex process. On one hand, no ground control points are visible due to heavy canopy. While on the other hand, precise georeferencing relies on ground control. As an alternative, we propose to use an available digital elevation model (DEM ) as control. The mathematical framework minimizes the difference between the lidar DEM and the reference DEM. Our solution consists of two steps: initial optimization to find reliable ground points through iterative filtering and georeferencing, and fine optimization to achieve precise georeferencing and lidar system calibration. Through this approach, the wave-form-derived DEM can best fit the reference DEM, with a mean of 0.937 m and standard deviation of 0.792 m, while the time-synchronization offset and boresight angles are simultaneously determined, i.e., self-calibrated. This development provides a novel georeferencing approach with self-calibration for lidar data without using conventional ground control points. Numéro de notice : A2021-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.1.43 Date de publication en ligne : 01/01/2021 En ligne : https://doi.org/10.14358/PERS.87.1.43 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96766
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 1 (January 2021) . - pp 43 - 52[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021011 SL Revue Centre de documentation Revues en salle Disponible Improving GEDI footprint geolocation using a high resolution digital terrain model / Anouk Schleich (2021)
Titre : Improving GEDI footprint geolocation using a high resolution digital terrain model Type de document : Article/Communication Auteurs : Anouk Schleich, Auteur ; Maxime Soma, Auteur ; Sylvie Durrieu, Auteur ; Cédric Vega , Auteur ; Jean-Pierre Renaud , Auteur ; Olivier Bouriaud , Auteur Editeur : Vienne [Autriche] : Technische Universität Wien Année de publication : 2021 Collection : Geowissenschaftliche Mitteilungen, ISSN 1811-8380 num. 104 Projets : TOSCA SLIM / Conférence : SilviLaser 2021, 17th conference on Lidar Applications for Assessing and Managing Forest Ecosystems 28/09/2021 30/09/2021 Vienne + online Autriche open access proceedings Importance : pp 179 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fauchée
[Termes IGN] géoréférencement
[Termes IGN] Global Ecosystem Dynamics Investigation lidar
[Termes IGN] modèle numérique de terrainRésumé : (auteur) [introduction] In 2018, NASA launched the Global Ecosystem Dynamics Investigation (GEDI) mission, a high resolution lidar system installed onboard the International Space Station (ISS). It is producing high quality 3D observations of the Earth surface structure, which are highly relevant to study forest ecosystems at a global scale (Qi et al. 2019). GEDI data is composed of 25 m diameter circular footprints for which the waveform of the received energy intensity returned by the ground is recorded. Each GEDI footprint is georeferenced and its positioning accuracy (for version 1 releases) is estimated at 15-20 m in planimetry with a systematic component of 8-10 m and a noise of the order of 8 m (1). A final horizontal geolocation accuracy of 8 m is expected after further processing in the final version (Dubayah et al. 2020). Compared to most other spatial satellites the ISS is much closer to earth, causing more variations in its orientation and altitude. Therefore, geolocating data acquired by ISS sensors is more diffucult than geolocating data aquired by satellites (Dou et al. 2014). An improved geolocation of GEDI data is mandatory to evaluate their quality, by comparison with other earth observation data or field measurements, and to further facilitate their integration in ecosystem monitoring approaches. We propose a method to improve the georeferencing of GEDI footprints using a precise Digital Terrain Model (DTM). Numéro de notice : C2021-053 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.34726/wim.1973 Date de publication en ligne : 01/12/2021 En ligne : https://doi.org/10.34726/wim.1973 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99223
Titre : LiDAR-based point clouds registration for localization in indoor environments Type de document : Thèse/HDR Auteurs : Ketty Favre, Auteur ; Luce Morin, Directeur de thèse ; Eric Marchand, Directeur de thèse Editeur : Rennes : Université de Rennes 1 Année de publication : 2021 Importance : 146 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Rennes 1, Spécialité Signal, Image, VisionLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de Gauss-Newton
[Termes IGN] appariement d'images
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espace intérieur
[Termes IGN] octree
[Termes IGN] Ransac (algorithme)
[Termes IGN] recalage de données localisées
[Termes IGN] scène intérieure
[Termes IGN] semis de points
[Termes IGN] superposition de donnéesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis deals with the problem of registration of 3D point clouds in indoor environments. Registration methods are proposed to obtain a compromise between time and accuracy. First, GNMR-ICP, a multi-resolution algorithm which robustly minimizes the point-to-plane distance between two point clouds using a Gauss-Newton method. The multi-resolution is done using an octree. On the ASL benchmark dataset, GNMR-ICP gives more accurate results than its equivalent using the small angle approximation (81% success rate against 43%). Computation times in structured environments are reduced (up to a factor of 2). Next we present NAP-ICP, an algorithm based on plane matching. Planes are matched using a score function based on the characteristics of pairs of planes. An additional point-to-plane registration is performed to ensure maximum accuracy. NAP-ICP registers 100% of the interior scenes of the ASL dataset and is more accurate than the evaluated state-of-the-art functions and is able to close the loops of the LOOP’IN dataset. Finally, PAR-ICP, a plane-based method where the matching is performed using a Random Forest is presented. PAR-ICP registers 100% of the interior scenes of the ASL dataset and is able to close the loops of LOOP’IN, allowing to generate incremental maps. Note de contenu : Introduction
1- Background
2- State of the art
3- Datasets
4- Multi-resolution registration of 3D point clouds
5- Plane-based registration of 3D point clouds
6- Learning-based plane matching for planet-to-plane
ConclusionNuméro de notice : 28635 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Signal, Image, Vision : Rennes 1 : 2021 Organisme de stage : Institut d'Électronique et de Télécommunications DOI : sans En ligne : http://www.theses.fr/2021REN1S059 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99666
Titre : A new stereo dense matching benchmark dataset for deep learning Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Marc Pierrot-Deseilligny , Auteur ; Ewelina Rupnik , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2-2021 Projets : AI4GEO / Conférence : ISPRS 2021, Commission 2, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 2 Importance : pp 405 - 412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] appariement de données localisées
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] jeu de données localisées
[Termes IGN] parangonnage
[Termes IGN] photogrammétrie aérienne
[Termes IGN] reconstruction 3DRésumé : (auteur) Stereo dense matching is a fundamental task for 3D scene reconstruction. Recently, deep learning based methods have proven effective on some benchmark datasets, for example Middlebury and KITTI stereo. However, it is not easy to find a training dataset for aerial photogrammetry. Generating ground truth data for real scenes is a challenging task. In the photogrammetry community, many evaluation methods use digital surface models (DSM) to generate the ground truth disparity for the stereo pairs, but in this case interpolation may bring errors in the estimated disparity. In this paper, we publish a stereo dense matching dataset based on ISPRS Vaihingen dataset, and use it to evaluate some traditional and deep learning based methods. The evaluation shows that learning-based methods outperform traditional methods significantly when the fine tuning is done on a similar landscape. The benchmark also investigates the impact of the base to height ratio on the performance of the evaluated methods. The dataset can be found in https://github.com/whuwuteng/benchmark_ISPRS2021. Numéro de notice : C2021-012 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2021-405-2021 Date de publication en ligne : 28/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-405-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98066 Optimisation des protocoles de numérisation 3D multi-capteurs et de fusion de données hétérogènes au sein de l'entreprise Premier plan / Elisa Gautron (2021)PermalinkQualification des données LiDAR GEDI pour le suivi de l’impact climatique sur la forêt de Südharz / Iris Jeuffrard (2021)PermalinkReconnaissance spécifique et cartographie des arbres de la canopée en forêt tropicale en Guyane française par fusion de données lidar et hyperspectrales appliquées aux besoins de la gestion forestière / Anthony Laybros (2021)PermalinkRelation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds / Yuan Li in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkPermalinkRemote sensing analysis of small scale dynamic phenomena in the atmospheric boundary layer / Kostas Cheliotis (2021)PermalinkPermalinkPermalinkStructure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring / Edoardo Grottoli in Remote sensing, vol 13 n° 1 (January-1 2021)PermalinkPermalink